Image process method, device, server, and storage medium

ABSTRACT

The present application provides an image process method, a device, a server, and a storage medium. Performing different processes to an enlarged image and then fusing a plurality of images after the different processes make the initial image experience multiple processes, which effectively mitigates sawtooth and artifacts phenomenon in the image while improving clarity of the image.

FIELD OF INVENTION

The present application relates to a field of image processing,especially to an image process method, a device, a server, and a storagemedium.

BACKGROUND OF INVENTION

A conventional super resolution rebuild algorithm comprises a superresolution rebuild based on interpolation, a super resolution rebuildbased on a degenerate model, and a super resolution rebuild based onlearning. The conventional super resolution algorithm mainly depends onbasic digital image process technologies to perform rebuild. The superresolution algorithm based on deep learning uses a super-resolutionconvolution neural network (SRCNN) model, enlarges an image of a lowresolution first and then restores through the model by an interpolationprocess.

The method based on interpolation deems each pixel on an image as apoint of an image plane, and employs known pixel messages to fit unknownpixel messages on the plane. Usually, the fitting is completed by apredefined transformation function or an interpolation core. Commonmethods based on interpolation include proximal interpolation process,bilinear interpolation process, and bicubic interpolation process.

SUMMARY OF INVENTION Technical Issue

A method based on interpolation is easy to calculate and understand, butalso has some obvious defects. First, it assumes that variation of apixel grayscale value is a continuous and smooth process, but actuallysuch assumption is not completely true. Second, during rebuild, a superresolution image is only calculated according to one predefinedtransformation function without consideration of a degraded degeneratemodel of an image, which usually results in a recovered image havingblur and sawtooth phenomenon.

Technical Solution

An objective of the present application is to provide an image processmethod, a device, a server, and a storage medium intended to solve anissue of the conventional image process still generating sawtooth andartifacts.

In a first aspect, the embodiment of the present application provides animage process method, the method includes:

-   -   enlarging an initial image to be processed to obtain an enlarged        image and calculating a gradient value and a gradient direction        corresponding to the enlarged image;    -   segmentally anti-aliasing filtering the enlarged image according        to the gradient direction to obtain an initial anti-aliasing        filtered image;    -   sharpening the enlarged image to obtain an initial sharpened        image; and    -   fusing the initial sharpened image and the initial anti-aliasing        filtered image to obtain a processed target image corresponding        to the initial image.

In a possible embodiment, the step of enlarging the initial image to beprocessed to obtain the enlarged image and calculating the gradientvalue and the gradient direction corresponding to the enlarged image,includes:

-   -   enlarging the initial image by a bicubic linear interpolation        algorithm to obtain the enlarged image; and    -   calculating a gradient value of the enlarged image by a        predetermined gradient operator and calculating a gradient        direction of the enlarged image.

In a possible embodiment, the step of calculating the gradient value ofthe enlarged image by the predetermined gradient operator andcalculating the gradient direction of the enlarged image, includes:

-   -   calculating a horizontal gradient value of the enlarged image by        a predetermined first gradient operator;    -   calculating a vertical gradient value of the enlarged image by a        predetermined second gradient operator; and    -   calculating and obtaining the gradient value of the enlarged        image and the gradient direction of the enlarged image according        to the horizontal gradient value and the vertical gradient        value.

In a possible embodiment, the step of calculating and obtaining thegradient direction of the enlarged image according to the horizontalgradient value and the vertical gradient value, includes:

-   -   solving a ratio of the horizontal gradient value to the vertical        gradient value by a tangent or arctangent function, wherein a        solving result is an angle corresponding to a gradient; and    -   determining the gradient direction according to the angle.

In a possible embodiment, the step of segmentally anti-aliasingfiltering the enlarged image according to the gradient direction toobtain the initial anti-aliasing filtered image, includes:

-   -   obtaining filtering operators corresponding to the gradient        direction, and anti-aliasing filtering the enlarged image        according to the filtering operators to obtain the initial        anti-aliasing filtered image; and    -   wherein the filtering operators corresponding to the gradient        direction in different angle ranges are different, and the        filtering operators are plural.

In a possible embodiment, the step of sharpening the enlarged image toobtain the initial sharpened image, includes:

sharpening the enlarged image by a predetermined sharpening operator toobtain the initial sharpened image.

In a possible embodiment, the step of sharpening the enlarged image bythe predetermined sharpening operator to obtain the initial sharpenedimage, includes:

-   -   a sharpening formula of sharpening the enlarged image by a        predetermined Laplacian operator to obtain a sharpened image,        which is as follows:

I _(lp) =I _(bc) +G _(lp)*(S _(lp) *I _(bc))

-   -   wherein the G_(lp) is a sharpening coefficient, the S_(lp) is        the predetermined Laplacian operator, the I_(bc) is a grayscale        value corresponding to a sampling point.

In a possible embodiment, the sharpening coefficient is an adjustablevalue, the image process method further includes:

-   -   adjusting a value of the sharpening coefficient G_(lp) to adjust        a sharpening degree of the enlarged image.

In a possible embodiment, the step of fusing the initial sharpened imageand the initial anti-aliasing filtered image to obtain the processedtarget image corresponding to the initial image, includes:

-   -   determining a first weigh corresponding to the initial        anti-aliasing filtered image and a second weight corresponding        to the initial sharpened image;    -   obtaining a target anti-aliasing filtered image according to the        first weight and the initial anti-aliasing filtered image;    -   obtaining a target sharpened image according to the second        weight and the initial sharpened image; and    -   fusing the target anti-aliasing filtered image and the target        sharpened image to obtain the target image.

In a possible embodiment, the gradient value is plural, and the step ofdetermining the first weigh corresponding to the initial anti-aliasingfiltered image and the second weight corresponding to the initialsharpened image, includes:

-   -   determining a maximum gradient value among the gradient values        and determining a first gradient value threshold and a second        gradient value threshold according to the maximum gradient        value;    -   obtaining a relationship of predetermined gradient value to        weight correspondence; and    -   determining the first weight and the second weight according to        the first gradient value threshold, the second gradient value        threshold, and the relationship of predetermined gradient value        to weight correspondence.

In a possible embodiment, the step of determining the maximum gradientvalue among the gradient values and determining the first gradient valuethreshold and the second gradient value threshold according to themaximum gradient value, includes:

E _(icor) =a*max(G);

E _(ith) =b*max(G);

-   -   wherein the maximum gradient value is max(G), the E_(icor) is        the first gradient value threshold, and the E_(ith) is the        second gradient value threshold.

In a second aspect, the embodiment of the present application providesan image process device, the device includes:

-   -   an enlarging module configured to enlarge an initial image to be        processed to obtain an enlarged image and configured to        calculate a gradient value and a gradient direction        corresponding to the enlarged image;    -   a filter module configured to segmentally anti-aliasing        filtering the enlarged image according to the gradient direction        to obtain an initial anti-aliasing filtered image;    -   a sharpening module configured to sharpen the enlarged image to        obtain an initial sharpened image; and    -   a fusion module configured to fuse the initial sharpened image        and the initial anti-aliasing filtered image to obtain a        processed target image corresponding to the initial image.

In a possible embodiment, the enlarging module is configured to:

-   -   enlarge the initial image by a bicubic linear interpolation        algorithm to obtain the enlarged image; and    -   calculate a gradient value of the enlarged image by a        predetermined gradient operator and calculate a gradient        direction of the enlarged image.

In a possible embodiment, the enlarging module is configured to:

-   -   calculate a horizontal gradient value of the enlarged image by a        predetermined first gradient operator;    -   calculate a vertical gradient value of the enlarged image by a        predetermined second gradient operator; and    -   calculate and obtain the gradient value of the enlarged image        and the gradient direction of the enlarged image according to        the horizontal gradient value and the vertical gradient value.

In a possible embodiment, the filter module is configured to: obtainfiltering operators corresponding to the gradient direction, andanti-aliasing filter the enlarged image according to the filteringoperators to obtain the initial anti-aliasing filtered image; and

-   -   wherein the filtering operators corresponding to the gradient        direction in different angle ranges are different, and the        filtering operators are plural.

In a possible embodiment, wherein the sharpening module is configuredto:

-   -   sharpen the enlarged image by a predetermined sharpening        operator to obtain the initial sharpened image.

In a possible embodiment, the fusion module is configured to:

-   -   determine a first weigh corresponding to the initial        anti-aliasing filtered image and a second weight corresponding        to the initial sharpened image;    -   obtain a target anti-aliasing filtered image according to the        first weight and the initial anti-aliasing filtered image;    -   obtain a target sharpened image according to the second weight        and the initial sharpened image; and    -   fuse the target anti-aliasing filtered image and the target        sharpened image to obtain the target image.

In a possible embodiment, the gradient value is plural, and the fusionmodule is configured to:

-   -   determine a maximum gradient value among the gradient values and        determine a first gradient value threshold and a second gradient        value threshold according to the maximum gradient value;    -   obtain a relationship of predetermined gradient value to weight        correspondence; and    -   determine the first weight and the second weight according to        the first gradient value threshold, the second gradient value        threshold, and the relationship of predetermined gradient value        to weight correspondence.

In a third aspect, the embodiment of the present application provides aserver, the server includes:

-   -   at least one processor;    -   a memory; and    -   at least one application program, wherein the at least one        application program is stored in the memory and is configured to        be implemented by the processor to perform any one of the image        process methods.

In a fourth aspect, the embodiment of the present application provides acomputer readable storage medium storing a computer program, wherein thecomputer program is loaded to a processor to implement the steps of anyone of the image process methods.

Advantages

The embodiment of the present application provides an image processmethod, a device, server, and a storage medium, including: first,enlarging an initial image to be processed to obtain an enlarged image,and a gradient value and a gradient direction corresponding to theenlarged image is calculated; Further, segmentally anti-aliasingfiltering the enlarged image according to the gradient direction toobtain an initial anti-aliasing filtered image; and sharpening theenlarged image is to obtain an initial sharpened image; finally, fusingthe initial sharpened image and the initial anti-aliasing filtered imageto obtain an initial image processed target image. Performing differentprocesses to an enlarged image and then fusing a plurality of imagesafter the different processes make the initial image experience multipleprocesses, which effectively mitigates sawtooth and artifacts phenomenonin the image while improving clarity of the image.

DESCRIPTION OF DRAWINGS

To more clearly elaborate on the technical solutions of embodiments ofthe present invention or prior art, appended figures necessary fordescribing the embodiments of the present invention or prior art will bebriefly introduced as follows. Apparently, the following appendedfigures are merely some embodiments of the present invention. A personof ordinary skill in the art may acquire other figures according to theappended figures without any creative effort.

FIG. 1 is a schematic view of a scenario of an image process systemprovided by the embodiment of the present application;

FIG. 2 is a schematic flowchart of an embodiment of an image processdevice provided by the embodiment of the present application;

FIG. 3 is an embodiment schematic flowchart of calculating imagegradient value and gradient direction provided by the embodiment of thepresent application;

FIG. 4 is a schematic view of a gradient direction provided by theembodiment of the present application;

FIG. 5 is a schematic flowchart of an embodiment of image fusionprovided by the embodiment of the present application;

FIG. 6 is an embodiment schematic view of the image process deviceprovided by the embodiment of the present application; and

FIG. 7 is a schematic structural view of a server relating to theembodiment of the present application.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The technical solution in the embodiment of the present application willbe clearly and completely described below with reference to theaccompanying drawings in the embodiments of the present application.Apparently, the described embodiments are merely some embodiments of thepresent application instead of all embodiments. According to theembodiments in the present application, all other embodiments obtainedby those skilled in the art without making any creative effort shallfall within the protection scope of the present application.

In the description of the present application, it should be understoodthat terminologies “center”, “longitudinal”, “transverse”, “length”,“width”, “thickness”, “upper”, “lower”, “front”, “rear”, “left”, “side”,“vertical”, “horizontal”, “top”, “bottom”, “inner”, “outer”,“clockwise”, “counterclockwise” for indicating relations of orientationor position are based on orientation or position of the accompanyingdrawings, are only for the purposes of facilitating description of thepresent application and simplifying the description instead ofindicating or implying that the referred device or element must have aspecific orientation or position, must to be structured and operatedwith the specific orientation or position. Therefore, they should not beunderstood as limitations to the present application. Furthermore,terminologies “first”, “second” are only for the purposes ofdescription, and cannot be understood as indication or implication ofcomparative importance or a number of technical features. Therefore, afeature limited with “first”, “second” can expressly or implicitlyinclude one or more features. In the description of the presentapplication, a meaning of “a plurality of” is two or more, unless thereis a clear and specific limitation otherwise.

In the present application, the word “exemplary” is used to mean“serving as an example, illustration or description”. Any embodimentdescribed as “exemplary” in the present invention is not necessarilyconstrued as preferred or more advantageous over other embodiments. Inorder to enable any person skilled in the art to implement and use thepresent invention, the following description is given. In the followingdescription, details are set forth for the purpose of explanation. Itshould be understood that a person of ordinary skill in the art willappreciate that the present invention may be implemented without the useof these specific details. In other instances, the known structures andprocesses are not elaborated to avoid unnecessary details from makingdescriptions of the present invention becomes ambiguous. Therefore, thepresent invention is not intended to be limited to the illustratedembodiment, but is consistent with the broadest scope of the principlesand features disclosed by the present invention.

It should be explained that because the embodiment of the presentapplication method is performed in an electronic apparatus, each processobject of each electronic apparatus is in form of data or messages, forexample, time is substantially time messages. It can be understood thatwhen size, number, position, etc. are mentioned in subsequentembodiments, corresponding data exist for convenience of process of theelectronic apparatus, which is not repeatedly described here.

The embodiment of the present application provides an image processmethod, a device, a server, and a storage medium that will be describedrespectively.

With reference to FIG. 1 , FIG. 1 is a schematic view of a scenario ofan image process system provided by the embodiment of the presentapplication. The image process system can include an electronicapparatus 100, and the electronic apparatus 100 can be integrated withan image process device, for example the electronic apparatus as shownin FIG. 1 .

In the embodiment of the present application, the electronic apparatus100 can be an individual server, and can be a server network or a servergroup consisting of servers. For example, the electronic apparatus 100described by the embodiment of the present application can include butis not limited to a computer, a network host, a single network server, aplurality of network server groups or a cloud server consisting of aplurality of servers. The cloud server consists of a huge amount ofcomputers or network servers based on cloud computing.

A person of ordinary skill in the art can understand that an applicationenvironment shown in FIG. 1 is only one application scenario of thepresent application solution and is not a limit to the applicationscenario of the present application solution. Other applicationenvironment can further include more or less electronic apparatuses thanshown in FIG. 1 . For example, FIG. 1 only shows one electronicapparatus. It can be understood that the image process system canfurther include one or a plurality of other servers, which is notlimited specifically here.

With reference to FIG. 1 , the image process system can further includea storage module 200 configured to storage data.

It should be explained that the schematic view of a scenario of theimage process system shown in FIG. 1 is only an example, the imageprocess system and scenario described by the embodiment of the presentapplication are for indicating a technical solution of the embodiment ofthe present application clearer, and would not constitute limits to thetechnical solution of the embodiment of the present applicationprovides. A person of ordinary skill in the art can understand that withdevelopment of the image process system and emergence of new applicationscenarios, the technical solution provided by the embodiment of thepresent application is also applicable for similar technical issues.

First, the embodiment of the present application provides an imageprocess method, an implementing body of the image process method is animage process device, the image process device is applied to anelectronic apparatus, and the image process method includes:

-   -   enlarging an initial image to be processed to obtain an enlarged        image and calculating a gradient value and a gradient direction        corresponding to the enlarged image; segmentally anti-aliasing        filtering the enlarged image according to the gradient direction        to obtain an initial anti-aliasing filtered image; sharpening        the enlarged image to obtain an initial sharpened image; and        fusing the initial sharpened image and the initial anti-aliasing        filtered image to obtain a processed target image corresponding        to the initial image.

With reference to FIG. 2 , FIG. 2 is a schematic flowchart of anembodiment of an image process device provided by the embodiment of thepresent application, the method includes steps as follows:

A step 21 includes enlarging an initial image to be processed to obtainan enlarged image and calculating a gradient value and a gradientdirection corresponding to the enlarged image.

In an embodiment of the present application, before the initial image isprocessed, enlarging or reducing the initial image at a certain degreeis required to improve a resolution and clarity of the image for laterprocesses.

In some embodiments, an interpolation algorithm can be used to enlargethe initial image. The interpolation algorithm is one of basic andimportant algorithms for image enlargement or reduction. In imageenlargement or reduction, an output image pixel point coordinateprobably corresponds to locations among several pixel points on an inputimage. At this time, a grayscale interpolation process is required tocalculate a grayscale value of the output point. A quality of theinterpolation algorithm also directly affects a degree of distortion ofthe image. Frequently used interpolation algorithms include thefollowing three: a proximal interpolation algorithm, a bilinearinterpolation algorithm, and a bicubic linear interpolation algorithm.

In an embodiment of the present application, the bicubic linearinterpolation algorithm can be used to enlarge the initial image toobtain an enlarged image. Then, a predetermined gradient operator isused to calculate a gradient value of the enlarged image to obtain agradient direction of the enlarged image.

The bicubic linear interpolation algorithm is used to enlarge theinitial image, because the bicubic linear interpolation uses grayscalevalues of sixteen points around a point to be sampled for threeinterpolations, which considers not only influence of the grayscale ofpoints directly adjacent to the sampling point, but also influence ofvariation rates of the grayscale values among adjacent points. Theenlarged image obtained by the bicubic linear interpolation is moreprecise.

Specific processes of algorithm enlarging the initial image by thebicubic linear interpolation can refer to the known art and is notlimited here.

A step 22 includes segmentally anti-aliasing filtering the enlargedimage according to the gradient direction to obtain an initialanti-aliasing filtered image.

A step 23 includes sharpening the enlarged image to obtain an initialsharpened image.

A step 24 includes fusing the initial sharpened image and the initialanti-aliasing filtered image to obtain a processed target imagecorresponding to the initial image.

For the embodiment of the present application, after the initial imageis enlarged to obtain an enlarged image, the enlarged image needs to beprocessed at different angles to obtain images processed differently,and then the processed images are fused to prevent sawtooth andartifacts generated by the image processed by a single process method.

In particular, the enlarged image can be segmentally anti-aliasingfiltered according to the gradient direction of the image to obtain ananti-aliasing filtered image.

The above embodiment mainly segmentally anti-aliasing filters theenlarged image, and sharpening the enlarged image is also needed toobtain a sharpened image. Then the sharpened image and a plurality ofgradient regions are fused such that a processed target image obtainedis not only sharpened but also filtered, which effectively improve aprecision of the target image.

It should be explained that in an embodiment of the present application,the segmentally anti-aliasing filtered image is not sharpened. Also, thesharpened image is not segmentally anti-aliasing filtered. However, theenlarged image is segmentally anti-aliasing filtered and sharpenedindividually, and then the segmentally anti-aliasing filtered image andthe sharpened image are fused to finally obtain a processed image. Assuch the enlarged image is only processed one time, and the obtainedimage has better precision and processing effect.

The image process method provided by the embodiment of the presentapplication includes: first, enlarging an initial image to be processedto obtain an enlarged image, and a gradient value and a gradientdirection corresponding to the enlarged image is calculated; Further,segmentally anti-aliasing filtering the enlarged image according to thegradient direction to obtain an initial anti-aliasing filtered image;and sharpening the enlarged image is to obtain an initial sharpenedimage; finally, fusing the initial sharpened image and the initialanti-aliasing filtered image to obtain an initial image processed targetimage. Performing different processes to an enlarged image and thenfusing a plurality of images after the different processes make theinitial image experience multiple processes, which effectively mitigatessawtooth and artifacts phenomenon in the image while improving clarityof the image.

In an embodiment of the present application, the gradient value of theimage refers to a variation speed of the grayscale value of the image.Because an image includes a plurality of pixels, grayscale valuescorresponding to any adjacent two of the pixels are different, whichresults in that actually each pixel in the image corresponds to agradient value. Therefore, n calculating the gradient value and thegradient direction of the image in the embodiment of the presentapplication actually is calculating the gradient value and the gradientdirection corresponding to each pixel of the image.

Further, because the gradient is a vector, the gradient value of theimage in the embodiment of the present application is actually a valuecorresponding to the gradient, and the gradient direction is a directioncorresponding to the gradient. Namely, the gradient value and thegradient direction are specific parameters in the gradient.

With reference to FIG. 3 , FIG. 3 is a an embodiment schematic flowchartof calculating image gradient value and gradient direction provided bythe embodiment of the present application, the calculation can include:

A step 31 includes calculating a horizontal gradient value of theenlarged image by a predetermined first gradient operator.

A step 32 includes calculating a vertical gradient value of the enlargedimage by a predetermined second gradient operator.

A step 33 includes calculating and obtaining the gradient value of theenlarged image and the gradient direction of the enlarged imageaccording to the horizontal gradient value and the vertical gradientvalue.

In an embodiment of the present application, calculating the gradientvalue and the gradient direction of the enlarged image first requires toperform sampling in the enlarged image, to obtain any sampling point.Calculating the gradient value or the gradient direction of the imagedescribed in the embodiment of the present application, actually iscalculating the gradient value and the gradient direction of thesampling point.

The embodiment of the present application provides different gradientoperators to calculate a horizontal gradient value and a verticalgradient value corresponding to the sampling point respectively. Inparticular, a Sobel operator can be used to calculate the horizontalgradient value and the vertical gradient value of the sampling point.wherein first gradient operator can be:

$\begin{matrix}{- 1} & 0 & 1 \\{S_{bx} = \left\lbrack {- 2} \right.} & 0 & {\left. 2 \right\rbrack/8} \\{- 1} & 0 & 1\end{matrix}$

The step of calculating the horizontal gradient value of the samplingpoint by the first gradient operator can be:

G_(x) is the horizontal gradient value corresponding to the samplingpoint, S_(bx) is the first gradient operator, I_(bc) is a grayscalevalue corresponding to the sampling point (or the pixel value).

The second gradient operator can be:

$\begin{matrix}{- 1} & 2 & 1 \\{S_{by} = \left\lbrack 0 \right.} & {0} & {\left. 0 \right\rbrack/8} \\{- 1} & {- 2} & {- 1}\end{matrix}$

Similarly, the step of calculating the vertical gradient value of thesampling point by the second gradient operator can be:

G _(y) =S _(by) *I _(bc)

G_(y) is the vertical gradient value corresponding to the samplingpoint, S_(by) is the second gradient operator, I_(bc) is the grayscalevalue corresponding to the sampling point (or the pixel value).

What are determined in the above embodiment are the horizontal gradientvalue and the vertical gradient value of the pixel, and actually thegradient value corresponding to the pixel needs to be obtained by thehorizontal gradient value and the vertical gradient value. Inparticular, the gradient value corresponding to the pixel can be:

G=√{square root over (G _(x) ² 30 G _(y) ²)}

Namely, in an embodiment of the present application, first thehorizontal gradient and the vertical gradient corresponding to the pixelneed to be determined first, and then the gradient value correspondingto the pixel is calculated according to the horizontal gradient and thevertical gradient.

After the horizontal gradient value and the vertical gradient valuecorresponding to the sampling point is calculated and obtained, thehorizontal gradient value and the vertical gradient value can be furtherused to calculate and obtain a gradient direction corresponding to thesampling point. In particular, the gradient direction corresponding tothe sampling point can be obtained as follows:

In an embodiment of the present application, a tangent or arctangentfunction can be used to determine a ratio of the horizontal gradientvalue to the vertical gradient value, and a result is an anglecorresponding to a gradient such that a gradient direction can bedetermined. In an embodiment of the present application, a value rangeof the gradient direction θ is (−π, π). With reference to FIG. 4 , FIG.4 is a schematic view of a gradient direction provided by the embodimentof the present application.

During actual processes of the image, the grayscale value (or the pixelvalue) corresponding to each pixel in the image is easier to determine.Therefore, a horizontal gradient value and a vertical gradient valuecorresponding to a pixel can be determined by a predetermined gradientoperator. After determination of the horizontal gradient value and thevertical gradient value, the gradient value and the gradient directioncorresponding to the pixel are further determined.

After determination of the gradient direction corresponding to thesampling point, segmentally anti-aliasing filtering the enlarged imageaccording to gradient direction is also needed. Because the gradientdirection is a direction of grayscale value increasing or decreasingfastest, segmentally anti-aliasing filtering the enlarged image based onthe gradient direction can effectively mitigate or remove sawtooth inthe image.

In some embodiments, it is the same to obtain filtering operatorscorresponding to the gradient direction to segmentally anti-aliasingfilter the enlarged image according to filtering operators. However, thegradient direction in a different angle range has a different filteringoperator such that the filtering operator is plural.

In particular, a formula for segmentally anti-aliasing filtering theenlarged image can be:

I _(AAFn) =AAF _(n) *I _(bc)

-   -   wherein I_(AAFn) is a grayscale value corresponding to the        filtered sampling point, and AAF_(n) is a filtering operator        provided by embodiment of the present application. Usually, the        filtering operators is plural. I_(bc) is an original grayscale        value corresponding to the sampling point.

A range of the gradient direction in the embodiment of the presentapplication is within (−π, π), and an absolute value of the gradientdirection is required for calculation, at this time, a value range inwhich θ=abs(θ) becomes (0, π).

In a specific embodiment, four different filtering operators can beselected according to a range of the gradient direction θ at (0, π), andcan be AAF₁, AAF₂, AAF₃, and AAF₄. Furthermore, the four differentgradient operator are:

$\begin{matrix}{0} & 0 & 0 \\{{AAF}_{1} = \left\lbrack 1 \right.} & 2 & {{\left. 1 \right\rbrack/4},} \\{0} & 0 & 0\end{matrix}$ $\begin{matrix}{0} & 0 & 1 \\{{AAF}_{2} = \left\lbrack 0 \right.} & 2 & {{\left. 0 \right\rbrack/4},} \\{1} & 0 & 0\end{matrix}$ $\begin{matrix}{0} & 1 & 0 \\{{AAF}_{3} = \left\lbrack 0 \right.} & 2 & {{\left. 0 \right\rbrack/4},} \\{0} & 1 & 0\end{matrix}$ $\begin{matrix}{1} & 0 & 0 \\{{AAF}_{4} = \left\lbrack 0 \right.} & 2 & {\left. 0 \right\rbrack/4} \\{0} & 0 & 1\end{matrix}$

At this time, a segmentally anti-aliasing filtering formula can be:

$I_{AAF} = \left\{ \begin{matrix}{{\left( {1 - t_{1}} \right)*I_{{AAF}1}} + {t_{1}*I_{{AAF}2}}} & {{0 < \theta \leq {\pi/4}},{{{t1} = {\theta/\left( {\pi/4} \right)}};}} \\{{\left( {1 - t_{2}} \right)*I_{{AAF}2}} + {t_{2}*I_{{AAF}3}}} & {{{\pi/4} < \theta \leq {\pi/2}},{{{t2} = {\left( {\theta - {\pi/4}} \right)/\left( {\pi/4} \right)}};}} \\{{\left( {1 - t_{3}} \right)*I_{{AAF}3}} + {t_{3}*I_{{AAF}4}}} & {{{\pi/2} < \theta \leq {\pi*3/4}},{{{t3} = {\left( {\theta - {\pi/2}} \right)/\left( {\pi/4} \right)}};}} \\{{\left( {1 - t_{4}} \right)*I_{{AAF}4}} + {t_{4}*I_{{AAF}1}}} & {{{\pi*3/4} < \theta \leq \pi},{{{t4} = {\left( {\theta - {\pi*3/4}} \right)/\left( {\pi/4} \right)}};}}\end{matrix} \right.$

The gradient direction corresponding to the sampling point θ=abs(θ)=π/4is taken as an example, inputting θ of π/4 into the above segmentallyanti-aliasing filtering formula can obtain a result of t1=1, thenI_(AAF)=(1−1)*I_(AAF1)+1*I_(AAF2)=I_(AAF2). According toI_(AAFn)=AAF_(n)*I_(bc), I_(AAF2) can be determined to accordinglydetermine the grayscale value corresponding to the sampling point aftersegmentally anti-aliasing filter under a condition of the sampling pointin which θ=π/4.

The above embodiment mainly segmentally anti-aliasing filters theenlarged image to achieve the objective of removing sawtooth shadow ofthe enlarged image. The embodiment of the present application alsorequires sharpening the image to obtain a sharpened image, and fusingthe sharpened image and the anti-aliasing filtered image.

In some embodiments, the step of sharpening the enlarged image to obtainthe initial sharpened image, can includeinclude: sharpening the enlargedimage by a predetermined sharpening operator to obtain the initialsharpened image.

In particular, a predetermined Laplacian operator can be used to sharpenthe enlarged image to obtain the sharpened image. A sharpening formulacan be:

I _(lp) =I _(bc) +G _(lp)*(S _(lp) *I _(bc))

-   -   wherein G_(lp) is a sharpening coefficient, and the sharpening        coefficient G_(lp) is an adjustable value. An image with a        different sharpening degree can be obtained by adjusting the        sharpening coefficient G_(lp) according to an actual sharpening        condition. In an embodiment of the present application,        sharpening coefficient G_(lp) can be adjusted within a range (1,        4). In a specific embodiment, the sharpening coefficient G_(lp)        can be 2, and S_(lp) is the predetermined Laplacian operator.        When the Laplacian operator is determined, S_(lp)*I_(bc) is also        determined. The embodiment of the present application mainly        adjusts the sharpened degree of the image by adjusting the        sharpening coefficient G_(lp). In the embodiment of the present        application, the sharpening coefficient G_(lp) is an adjustable        value.

In the above embodiment, I_(bc) is also a grayscale value correspondingto a sampling point, and the embodiment of the present application firstemploys the filtering operators to process the grayscale valuecorresponding to the sampling point, and then multiplies the value bythe adjustable sharpening coefficient G_(lp).

In a specific embodiment, Laplacian operator S_(lp) can be:

$\begin{matrix}{0} & {- 1} & 0 \\{S_{lp} = \left\lbrack {- 1} \right.} & 4 & {\left. {- 1} \right\rbrack/8} \\{0} & {- 1} & 0\end{matrix}$

It should be explained that specific values of the first gradientoperator, second gradient operator, and Laplacian operator provided bythe embodiment of the present application are commonly used values. Inother embodiments, the first gradient operator, second gradientoperator, and Laplacian operator can also be other values, which is notlimited here.

The above embodiment segmentally anti-aliasing filters the enlargedimage to obtain an anti-aliasing filtered image, and sharpens theenlarged image to obtain a sharpened image. The embodiment of thepresent application also needs to fuse the anti-aliasing filtered imageand the sharpened image such that the obtained image is not onlyanti-aliasing filtered but also sharpened.

With reference to FIG. 5 , FIG. 5 is a schematic flowchart of anembodiment of image fusion provided by the embodiment of the presentapplication, the image fusion can include:

A step 51 includes determining a first weigh corresponding to theinitial anti-aliasing filtered image and a second weight correspondingto the initial sharpened image.

In particular, in an embodiment of the present application, when theanti-aliasing filtered image and the sharpened image are fused, fordifferent pixels, the weight corresponding to the initial anti-aliasingfiltered image and the weight corresponding to the initial sharpenedimage are different.

In some embodiments, the step of determining the first weighcorresponding to the initial anti-aliasing filtered image and the secondweight corresponding to the initial sharpened image, can include:determining a maximum gradient value among the gradient values anddetermining a first gradient value threshold and a second gradient valuethreshold according to the maximum gradient value; obtaining arelationship of predetermined gradient value to weight correspondence;and determining the first weight and the second weight according to thefirst gradient value threshold, the second gradient value threshold, andthe relationship of predetermined gradient value to weightcorrespondence.

In particular, because the present application determines the gradientvalue and the gradient direction corresponding to each pixel in theenlarged image, the gradient value is plural actually. The differentgradient values have a maximum gradient value in which the value ismaximal.

The present application also provides a relationship of predeterminedgradient value to weight correspondence. In the relationship ofpredetermined gradient value to weight correspondence, when the gradientvalue corresponding to the sampling point are in different angle ranges,the first weight and the second weight corresponding to the samplingpoint are also different. Therefore, the initial anti-aliasing filteredimage and the initial sharpened image to be fused are also different.

In the mean time, the embodiment of the present application alsodetermines a first gradient value threshold and a second gradient valuethreshold according to the maximum gradient value. Further calculatingthe first gradient value threshold and the second gradient valuethreshold can obtain a specific weight value of the above relationshipof predetermined gradient value to weight correspondence.

In a specific embodiment, maximum gradient value can be: max(G). Thefirst gradient value threshold and the second gradient value thresholdobtained according to the maximum gradient value can be:

E _(icor) =a*max(G);

E _(ith) =b*max(G);

-   -   wherein E_(icor) is a first gradient value threshold, and        E_(ith) is a second gradient value threshold; values of a and b        can be set at will; usually, the value of a is less than the        value of b. In a specific embodiment, a can be 0.2, and b can be        0.4.

The step of determining the first weight and the second weight accordingto the first gradient value threshold, the second gradient valuethreshold, and the relationship of predetermined gradient value toweight correspondence can be:

$E_{i} = \left\{ \begin{matrix}0 & {G \leq E_{icor}} \\{\left( {G - E_{icor}} \right)/\left( {E_{ith} - E_{icor}} \right)} & {E_{icor} < G < E_{ith}} \\1 & {G \geq E_{ith}}\end{matrix} \right.$

In the above formula, E_(i) is a weight parameter; The first weight canbe 1−E_(i), second weight is E_(i).

A step 52 includes obtaining a target anti-aliasing filtered imageaccording to the first weight and the initial anti-aliasing filteredimage.

At this time, the step obtaining a target anti-aliasing filtered imageaccording to the first weight and the initial anti-aliasing filteredimage can be:

(1−E_(i))*I_(AAF).

A step 53 includes obtaining a target sharpened image according to thesecond weight and the initial sharpened image.

The step of obtaining a target sharpened image according to the secondweight and the initial sharpened image can be: E_(i)*I_(lp).

A step 54 includes fusing the target anti-aliasing filtered image andthe target sharpened image to obtain the target image.

At this time, the step of fusing the target anti-aliasing filtered imageand the target sharpened image to obtain the target image can be:

I _(fusion)=(1−E _(i))*I _(AAF) +E _(i) *I _(lp)

The above formula is an image fusion formula provided by the embodimentof the present application. I_(fusion) is a pixel grayscale valuecorresponding to the fused sampling point, (1−E_(i))*I_(AAF) is a targetanti-aliasing filtered image, E_(i)*I_(lp) is a target sharpened image.Furthermore, the weight parameter E_(i) needs to be determined accordingto the maximum gradient value in the enlarged image.

The embodiment of the present application also provides an image processdevice. With reference to FIG. 6 , FIG. 6 is a an embodiment schematicview of the image process device provided by the embodiment of thepresent application, the image process device can include:

-   -   an enlarging module 601 configured to enlarging an initial image        to be processed to obtain an enlarged image, and calculate a        gradient value and a gradient direction corresponding to the        enlarged image;    -   a filter module 602 configured to segmentally anti-aliasing        filtering the enlarged image according to the gradient direction        to obtain an initial anti-aliasing filtered image;    -   a sharpening module 603 configured to sharpen the enlarged image        to obtain an initial sharpened image; and    -   a fusion module 604 configured to fuse the initial sharpened        image and the initial anti-aliasing filtered image to obtain a        processed target image corresponding to the initial image.

The image process device provided by the embodiment of the presentapplication includes: first, enlarging an initial image to be processedto obtain an enlarged image, and a gradient value and a gradientdirection corresponding to the enlarged image is calculated; Further,segmentally anti-aliasing filtering the enlarged image according to thegradient direction to obtain an initial anti-aliasing filtered image;and sharpening the enlarged image is to obtain an initial sharpenedimage; finally, fusing the initial sharpened image and the initialanti-aliasing filtered image to obtain an initial image processed targetimage. Performing different processes to an enlarged image and thenfusing a plurality of images after the different processes make theinitial image experience multiple processes, which effectively mitigatessawtooth and artifacts phenomenon in the image while improving clarityof the image.

In some embodiments, the enlarging module 601 can be specificallyconfigured to: enlarge the initial image by a bicubic linearinterpolation algorithm to obtain the enlarged image; and calculate agradient value of the enlarged image by a predetermined gradientoperator and calculate a gradient direction of the enlarged image.

In some embodiments, the enlarging module 601 can be specificallyconfigured to: calculate a horizontal gradient value of the enlargedimage by a predetermined first gradient operator; calculate a verticalgradient value of the enlarged image by a predetermined second gradientoperator; and calculate and obtain the gradient value of the enlargedimage and the gradient direction of the enlarged image according to thehorizontal gradient value and the vertical gradient value.

In some embodiments, the filter module 602 can be specificallyconfigured to: obtain filtering operators corresponding to the gradientdirection, and anti-aliasing filter the enlarged image according to thefiltering operators to obtain the initial anti-aliasing filtered image;and

-   -   wherein the filtering operators corresponding to the gradient        direction in different angle ranges are different, and the        filtering operators are plural.

In some embodiments, the sharpening module 603 can be specificallyconfigured to: sharpen the enlarged image by a predetermined sharpeningoperator to obtain the initial sharpened image.

In some embodiments, the fusion module 604 can be specificallyconfigured to: determine a first weigh corresponding to the initialanti-aliasing filtered image and a second weight corresponding to theinitial sharpened image; obtain a target anti-aliasing filtered imageaccording to the first weight and the initial anti-aliasing filteredimage; obtain a target sharpened image according to the second weightand the initial sharpened image; and fuse the target anti-aliasingfiltered image and the target sharpened image to obtain the targetimage.

In some embodiments, the gradient value is plural; the fusion module 604can be specifically configured to: determine a maximum gradient valueamong the gradient values and determine a first gradient value thresholdand a second gradient value threshold according to the maximum gradientvalue; obtain a relationship of predetermined gradient value to weightcorrespondence; and determine the first weight and the second weightaccording to the first gradient value threshold, the second gradientvalue threshold, and the relationship of predetermined gradient value toweight correspondence.

The embodiment of the present application also provides an electronicapparatus integrated with any one of the image process devices providedby the embodiment of the present application. With reference to FIG. 7 ,FIG. 7 is a schematic structural view of a server relating to theembodiment of the present application, and it is specifically describedthat:

The electronic apparatus can include at least one the processor 701 of aprocess core, at least one memory 702 of a computer readable storagemedium, a power source 703, an input unit 704, etc. A person of ordinaryskill in the art can understand that, the electronic apparatus structureshown in the figures has no limit to the electronic apparatus, more orless parts than as shown in the figures, combination of some parts ordifferent arrangements of the parts, can be included. wherein:

The processor 701 is a control center of the electronic apparatus, usesvarious interfaces and lines to connect each part of the entireelectronic apparatus, and implements various functions of the electronicapparatus and processes data by operating or implementing softwareprograms and/or modules stored in the memory 702 and calling data storedthe memory 702 to monitor the entire electronic apparatus. Optionally,the processor 701 can include one or a plurality of process cores.Preferably, the processor 701 can be integrated with an applicationprocessor and a modem processor, wherein the application processor ismainly configured to process an operation system, a user interface, andan application program. The modem processor is mainly configured toprocess wireless communication. It can be understood that the above themodem processor can be out of the processor 701.

The memory 702 can be configured to store a software program and module.The processor 701, by implementing a software program and module storedin the memory 702, implements various function applications and dataprocesses. The memory 702 can mainly include a storage program regionand a storage data region. The storage program region can store anoperation system, at least one application program required by functions(for example, audio play function, image play function). The storagedata region can store according to data built for utility of electronicapparatus. Furthermore, the memory 702 can include a high-speed randomaccess memory, can further include a non-volatile memory, for example,at least one disk storage device, flash device, or other volatile solidstate storage device. Accordingly, the memory 702 can further include amemory controller to allow the processor 701 to access the memory 702.

The electronic apparatus further includes a power source 703 poweringeach part. Preferably, the power source 703 can be logically connectedto the processor 701 through a power management system such thatmanagements of charging, discharging and power consumption managementare achieved through the power management system. The power source 703can further include any assembly such as at least one direct current oralternate current power source, recharge system, power sourcemalfunction detective circuit, power source converter or inverter, powersource state indicator, etc.

The electronic apparatus can further include an input unit 704. Theinput unit 704 can be configured to receive inputted digital orcharacter message, and generate signal input of keyboard, mouse,joystick, or optical or tracking ball related to user configuration andfunction control.

Although not shown, the electronic apparatus can further include adisplay unit, which is not repeatedly described here. In particular, inthe present embodiment, the processor 701 in the electronic apparatuswould, according to the following instructions, loads executable filescorresponding to processes of one or more application programs in to thememory 702, and the processor 701 performs the application programstored in the memory 702 to implement various functions as follows:

-   -   enlarging an initial image to be processed to obtain an enlarged        image and calculating a gradient value and a gradient direction        corresponding to the enlarged image; segmentally anti-aliasing        filtering the enlarged image according to the gradient direction        to obtain an initial anti-aliasing filtered image; sharpening        the enlarged image to obtain an initial sharpened image; and        fusing the initial sharpened image and the initial anti-aliasing        filtered image to obtain a processed target image corresponding        to the initial image.

A person of ordinary skill in the art can understand that some or allsteps of each method of the above embodiment can be accomplished byinstructions or by related hardware controlled by instructions. Theinstructions can be stored in a computer readable storage medium, and beloaded to and performed by a processor.

As such, the embodiment of the present application provides a computerreadable storage medium, the storage medium can include: read onlymemory (ROM), random access memory (RAM), hard disc, or compact disc. Acomputer program is stored thereon, and the computer program is loadedto the processor to perform steps of any one of image process methodsprovided by the embodiment of the present application. For example, thecomputer program is loaded to the processor to perform the followingsteps:

-   -   enlarging an initial image to be processed to obtain an enlarged        image and calculating a gradient value and a gradient direction        corresponding to the enlarged image; segmentally anti-aliasing        filtering the enlarged image according to the gradient direction        to obtain an initial anti-aliasing filtered image; sharpening        the enlarged image to obtain an initial sharpened image; and        fusing the initial sharpened image and the initial anti-aliasing        filtered image to obtain a processed target image corresponding        to the initial image.

In the above-mentioned embodiments, the descriptions of the variousembodiments are focused. For the details of the embodiments notdescribed, reference may be made to the related descriptions of theother embodiments, which is not repeatedly described here.

In specific implementation, each of the above units or structures may beimplemented as a separate entity, or may be any combination, andimplemented as the same entity or a plurality of entities.

The specific implementation of the above units or structures refer tothe previous method embodiment and will not be described repeatedly.

The image process method, device, server, and storage medium provided bythe embodiment of the present application are described in detail asabove. In the specification, the specific examples are used to explainthe principle and embodiment of the present application. The abovedescription of the embodiments is only used to help understand themethod of the present application and its spiritual idea. Meanwhile, forthose skilled in the art, according to the present the idea ofinvention, changes will be made in specific embodiment and application.In summary, the contents of this specification should not be construedas limiting the present application.

1. An image process method, wherein the method comprises: enlarging aninitial image to be processed to obtain an enlarged image andcalculating a gradient value and a gradient direction corresponding tothe enlarged image; segmentally anti-aliasing filtering the enlargedimage according to the gradient direction to obtain an initialanti-aliasing filtered image; sharpening the enlarged image to obtain aninitial sharpened image; and fusing the initial sharpened image and theinitial anti-aliasing filtered image to obtain a processed target imagecorresponding to the initial image.
 2. The image process methodaccording to claim 1, wherein the step of enlarging the initial image tobe processed to obtain the enlarged image and calculating the gradientvalue and the gradient direction corresponding to the enlarged image,comprises: enlarging the initial image by a bicubic linear interpolationalgorithm to obtain the enlarged image; and calculating a gradient valueof the enlarged image by a predetermined gradient operator andcalculating a gradient direction of the enlarged image.
 3. The imageprocess method according to claim 2, wherein the step of calculating thegradient value of the enlarged image by the predetermined gradientoperator and calculating the gradient direction of the enlarged image,comprises: calculating a horizontal gradient value of the enlarged imageby a predetermined first gradient operator; calculating a verticalgradient value of the enlarged image by a predetermined second gradientoperator; and calculating and obtaining the gradient value of theenlarged image and the gradient direction of the enlarged imageaccording to the horizontal gradient value and the vertical gradientvalue.
 4. The image process method according to claim 3, wherein thestep of calculating and obtaining the gradient direction of the enlargedimage according to the horizontal gradient value and the verticalgradient value, comprises: solving a ratio of the horizontal gradientvalue to the vertical gradient value by a tangent or arctangentfunction, wherein a solving result is an angle corresponding to agradient; and determining the gradient direction according to the angle.5. The image process method according to claim 1, wherein the step ofsegmentally anti-aliasing filtering the enlarged image according to thegradient direction to obtain the initial anti-aliasing filtered image,comprises: obtaining filtering operators corresponding to the gradientdirection, and anti-aliasing filtering the enlarged image according tothe filtering operators to obtain the initial anti-aliasing filteredimage; and wherein the filtering operators corresponding to the gradientdirection in different angle ranges are different, and the filteringoperators are plural.
 6. The image process method according to claim 1,wherein the step of sharpening the enlarged image to obtain the initialsharpened image, comprises: sharpening the enlarged image by apredetermined sharpening operator to obtain the initial sharpened image.7. The image process method according to claim 6, wherein the step ofsharpening the enlarged image by the predetermined sharpening operatorto obtain the initial sharpened image, comprises: a sharpening formulaof sharpening the enlarged image by a predetermined Laplacian operatorto obtain a sharpened image, which is as follows:I _(lp) =I _(bc) +G _(lp)*(S _(lp) *I _(bc)) wherein the G_(lp) is asharpening coefficient, the S_(lp) is the predetermined Laplacianoperator, the I_(bc) is a grayscale value corresponding to a samplingpoint.
 8. The image process method according to claim 7, wherein thesharpening coefficient is an adjustable value, the image process methodfurther comprises: adjusting a value of the sharpening coefficientG_(lp) to adjust a sharpening degree of the enlarged image.
 9. The imageprocess method according to claim 1, wherein the step of fusing theinitial sharpened image and the initial anti-aliasing filtered image toobtain the processed target image corresponding to the initial image,comprises: determining a first weigh corresponding to the initialanti-aliasing filtered image and a second weight corresponding to theinitial sharpened image; obtaining a target anti-aliasing filtered imageaccording to the first weight and the initial anti-aliasing filteredimage; obtaining a target sharpened image according to the second weightand the initial sharpened image; and fusing the target anti-aliasingfiltered image and the target sharpened image to obtain the targetimage.
 10. The image process method according to claim 9, wherein thegradient value is plural, and the step of determining the first weighcorresponding to the initial anti-aliasing filtered image and the secondweight corresponding to the initial sharpened image, comprises:determining a maximum gradient value among the gradient values anddetermining a first gradient value threshold and a second gradient valuethreshold according to the maximum gradient value; obtaining arelationship of predetermined gradient value to weight correspondence;and determining the first weight and the second weight according to thefirst gradient value threshold, the second gradient value threshold, andthe relationship of predetermined gradient value to weightcorrespondence.
 11. The image process method according to claim 10,wherein the step of determining the maximum gradient value among thegradient values and determining the first gradient value threshold andthe second gradient value threshold according to the maximum gradientvalue, comprises:E _(icor) =a*max(G);E _(ith) =b*max(G); wherein the maximum gradient value is max(G), theE_(icor) is the first gradient value threshold, and the E_(ith) is thesecond gradient value threshold.
 12. An image process device, whereinthe device comprises: an enlarging module configured to enlarge aninitial image to be processed to obtain an enlarged image and configuredto calculate a gradient value and a gradient direction corresponding tothe enlarged image; a filter module configured to segmentallyanti-aliasing filtering the enlarged image according to the gradientdirection to obtain an initial anti-aliasing filtered image; asharpening module configured to sharpen the enlarged image to obtain aninitial sharpened image; and a fusion module configured to fuse theinitial sharpened image and the initial anti-aliasing filtered image toobtain a processed target image corresponding to the initial image. 13.The image process device according to claim 12, wherein the enlargingmodule is configured to: enlarge the initial image by a bicubic linearinterpolation algorithm to obtain the enlarged image; and calculate agradient value of the enlarged image by a predetermined gradientoperator and calculate a gradient direction of the enlarged image. 14.The image process device according to claim 13, wherein the enlargingmodule is configured to: calculate a horizontal gradient value of theenlarged image by a predetermined first gradient operator; calculate avertical gradient value of the enlarged image by a predetermined secondgradient operator; and calculate and obtain the gradient value of theenlarged image and the gradient direction of the enlarged imageaccording to the horizontal gradient value and the vertical gradientvalue.
 15. The image process device according to claim 12, wherein thefilter module is configured to: obtain filtering operators correspondingto the gradient direction, and anti-aliasing filter the enlarged imageaccording to the filtering operators to obtain the initial anti-aliasingfiltered image; and wherein the filtering operators corresponding to thegradient direction in different angle ranges are different, and thefiltering operators are plural.
 16. The image process device accordingto claim 12, wherein the sharpening module is configured to: sharpen theenlarged image by a predetermined sharpening operator to obtain theinitial sharpened image.
 17. The image process device according to claim12, wherein the fusion module is configured to: determine a first weighcorresponding to the initial anti-aliasing filtered image and a secondweight corresponding to the initial sharpened image; obtain a targetanti-aliasing filtered image according to the first weight and theinitial anti-aliasing filtered image; obtain a target sharpened imageaccording to the second weight and the initial sharpened image; and fusethe target anti-aliasing filtered image and the target sharpened imageto obtain the target image.
 18. The image process device according toclaim 17, wherein the gradient value is plural, and the fusion module isconfigured to: determine a maximum gradient value among the gradientvalues and determine a first gradient value threshold and a secondgradient value threshold according to the maximum gradient value; obtaina relationship of predetermined gradient value to weight correspondence;and determine the first weight and the second weight according to thefirst gradient value threshold, the second gradient value threshold, andthe relationship of predetermined gradient value to weightcorrespondence.
 19. A server, wherein the server comprises: at least oneprocessor; a memory; and at least one application program, wherein theat least one application program is stored in the memory and isconfigured to be implemented by the processor to perform the imageprocess method according to claim
 1. 20. (canceled)